spark-python-data-source

To build a custom PySpark data source connector, implement the DataSourceReader and DataSourceWriter interfaces to handle reading from and writing to external systems. You must manage partitioning, option handling, and executor-local imports to ensure reliable Spark DataFrame integration.

Structured Streaming offset management tracks incremental read positions by implementing DataSourceStreamReader and DataSourceStreamWriter. This handles continuous data ingestion from external systems, ensuring resilient exactly-once-style streaming by persisting offsets across micro-batches.

PySpark custom connector serialization errors occur when inheritance is not flat or third-party imports happen on the driver. To fix this, keep inheritance single-level and move third-party imports to executor-local scopes to avoid serialization failures during distributed execution.

Yes, you can implement both batch and Structured Streaming reads in a custom Spark data source by implementing the respective DataSourceReader and DataSourceStreamReader interfaces. This requires handling bidirectional type conversion and authentication priority chains for both contexts.

The best way to handle authentication in a custom PySpark data source connector is implementing an authentication priority chain with fallbacks. This pattern securely manages credentials across driver and executor boundaries while maintaining robust error handling during external system access.

To convert external API data types to a Spark schema in a custom data source, implement bidirectional type conversion within your DataSourceReader. This maps external system schemas to Spark DataFrame types, ensuring data compatibility during both batch reads and streaming ingestion.